The First Decade of Long-Lead U.S. Seasonal Forecasts

The First Decade of Long-Lead U.S. Seasonal Forecasts The first 10 yr (issued starting in mid-December 1994) of official, long-lead (out to 1 yr) U.S. 3-month mean temperature and precipitation forecasts are verified using a categorical skill score. Through aggregation of forecasts over overlapping 3-month target periods and/or multiple leads, we obtain informative results about skill improvements, skill variability (by lead, season, location, variable, and situation), skill sources, and potential forecast utility. The forecasts clearly represent advances over zero-lead forecasts issued prior to 1995. But our most important result is that skill hardly varies by lead time all the way out to 1 yr, except for cold-season forecasts under strong El Nio or La Nia (ENSO) conditions. The inescapable conclusion is that this lead-independent skill comes from use of long-term trends to make the forecasts and we show that these trends are almost entirely associated with climate change. However, we also argue that climate change is not yet being optimally taken into account, so there is scope for improving the quality of the forecasts. Practically all other skill in the forecasts comes from exploitation of strong and predictable ENSO episodes for winter forecasts, out to a 6.5-month lead for precipitation and beyond 8.5 months for temperature. Apparently other sources of skill supported by existing research, including predictability inherent in weaker ENSO episodes and interactive feedbacks between the extratropical atmosphere and underlying surfaces, do not materially contribute to positive forecast performance. Compared to strong ENSO and climate change signals, other sources are too weak, unreliable, or poorly understood to detect an impact. Another consequence of the clear attribution of skill is that often-observed high regional/seasonal skills imply that the forecasts can be unambiguously valuable to a wide range of users. With these findings, steps (some immediate) can be taken to improve both the skill and usability of official long-lead forecasts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

The First Decade of Long-Lead U.S. Seasonal Forecasts

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
D.O.I.
10.1175/2008BAMS2488.1
Publisher site
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Abstract

The first 10 yr (issued starting in mid-December 1994) of official, long-lead (out to 1 yr) U.S. 3-month mean temperature and precipitation forecasts are verified using a categorical skill score. Through aggregation of forecasts over overlapping 3-month target periods and/or multiple leads, we obtain informative results about skill improvements, skill variability (by lead, season, location, variable, and situation), skill sources, and potential forecast utility. The forecasts clearly represent advances over zero-lead forecasts issued prior to 1995. But our most important result is that skill hardly varies by lead time all the way out to 1 yr, except for cold-season forecasts under strong El Nio or La Nia (ENSO) conditions. The inescapable conclusion is that this lead-independent skill comes from use of long-term trends to make the forecasts and we show that these trends are almost entirely associated with climate change. However, we also argue that climate change is not yet being optimally taken into account, so there is scope for improving the quality of the forecasts. Practically all other skill in the forecasts comes from exploitation of strong and predictable ENSO episodes for winter forecasts, out to a 6.5-month lead for precipitation and beyond 8.5 months for temperature. Apparently other sources of skill supported by existing research, including predictability inherent in weaker ENSO episodes and interactive feedbacks between the extratropical atmosphere and underlying surfaces, do not materially contribute to positive forecast performance. Compared to strong ENSO and climate change signals, other sources are too weak, unreliable, or poorly understood to detect an impact. Another consequence of the clear attribution of skill is that often-observed high regional/seasonal skills imply that the forecasts can be unambiguously valuable to a wide range of users. With these findings, steps (some immediate) can be taken to improve both the skill and usability of official long-lead forecasts.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Jun 17, 2008

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